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oura-ring-analysis

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Personal health analytics with Oura Ring data: Bayesian sleep analysis, circadian patterns, and cardiovascular metrics

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# Oura Ring Data Analysis
I am often asked two questions: what is that on your finger? what do you learn from oura ring?

Here are some rudimentary analytics using 2+ years of my own Oura Ring data. This project applies statistical and Bayesian methods to extract actionable insights from sleep, heart rate, and activity metrics.

## Key Findings

- **Sleep quality improved 16%** from 2023 to 2025 (67 → 78 avg score)
- **Resting heart rate of 50 bpm** indicates strong cardiovascular fitness
- **Deep sleep averages 107 min/night** — well above the 60-90 min recommendation
- **HRV of 55ms** reflects healthy autonomic nervous system function

## Featured Analyses

### 1. Year-Over-Year Health Trends

![Monthly Heatmap](https://raw.githubusercontent.com/xiaolong-y/oura-ring-analysis/main/visualizations/03_monthly_heatmap.png)

Heatmap reveals consistent improvement across all three core metrics. Sleep scores show the clearest upward trajectory, with 2025 averaging 5-10 points higher than 2023 across all months.

### 2. Circadian Heart Rate Pattern

![Circadian Rhythm](https://raw.githubusercontent.com/xiaolong-y/oura-ring-analysis/main/visualizations_heartrate/01_circadian_rhythm.png)

24-hour heart rate analysis reveals a textbook circadian pattern: lowest at 9 AM (49.7 bpm) after sleep-driven parasympathetic recovery, highest at 2 PM (62.6 bpm) during peak metabolic activity. The tight variance during sleep hours indicates consistent sleep quality.

### 3. Sleep Architecture Analysis

![Sleep Summary](https://raw.githubusercontent.com/xiaolong-y/oura-ring-analysis/main/publication_figures/fig_summary_publication.png)

Multi-panel analysis of sleep stage dynamics:
- **Transition matrix** shows healthy stage cycling with 72% deep sleep stability
- **First vs. second half** distribution matches expected physiology (more deep sleep early, more REM late)
- **Regime shift detection** identifies periods of sleep pattern changes

## Methods

| Analysis | Technique |
|----------|-----------|
| Trend decomposition | STL (Seasonal-Trend-Loess) |
| Parameter estimation | Bayesian inference with MCMC |
| Anomaly detection | Isolation Forest |
| Change point detection | Bayesian Online Change Point Detection |
| Clustering | K-means with t-SNE visualization |

## Repository Structure

```
├── *.json                 # Raw Oura API exports
├── *_analysis.py          # Analysis scripts
├── visualizations/        # Basic exploratory plots
├── visualizations_*/      # Domain-specific analyses
├── publication_figures/   # Publication-ready figures
└── *.html                 # Interactive reports
```

## Reports

- `2025-12-16_oura_analysis_report.html` — Comprehensive overview
- `2025-12-16_bayesian_sleep_analysis_report.html` — Bayesian sleep modeling
- `2025-12-16_heartrate_deep_dive_report.html` — Heart rate analysis
- `sleep_health_report.pdf` — LaTeX-formatted health report

## Requirements

```
pandas numpy scipy matplotlib seaborn scikit-learn
```

---

*Analysis performed with Claude Code*

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